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dc.contributor.authorIvonin, Leonid
dc.contributor.authorHuang-Ming, Chang
dc.contributor.authorDíaz Boladeras, Marta
dc.contributor.authorCatalà Mallofré, Andreu
dc.contributor.authorChen, Wei
dc.contributor.authorRauterberg, Mattias
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Organització d'Empreses
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2019-08-30T07:33:14Z
dc.date.issued2015-03-04
dc.identifier.citationIvonin, L. [et al.]. Beyond cognition and affect: sensing the unconscious. "Behaviour & information technology", 4 Març 2015, vol. 34, núm. 3, p. 220-238.
dc.identifier.issn0144-929X
dc.identifier.urihttp://hdl.handle.net/2117/167819
dc.description.abstractIn the past decade, research on human-computer interaction has embraced psychophysiological user interfaces that enhance awareness of computers about conscious cognitive and affective states of users and increase their adaptive capabilities. Still, human experience is not limited to the levels of cognition and affect but extends further into the realm of universal instincts and innate behaviours that form the collective unconscious. Patterns of instinctual traits shape archetypes that represent images of the unconscious. This study investigated whether seven various archetypal experiences of users lead to recognisable patterns of physiological responses. More specifically, the potential of predicting the archetypal experiences by a computer from physiological data collected with wearable sensors was evaluated. The subjects were stimulated to feel the archetypal experiences and conscious emotions by means of film clips. The physiological data included measurements of cardiovascular and electrodermal activities. Statistical analysis indicated a significant relationship between the archetypes portrayed in the videos and the physiological responses. Data mining methods enabled us to create between-subject prediction models that were capable of classifying four archetypes with an accuracy of up to 57.1%. Further analysis suggested that classification performance could be improved up to 70.3% in the case of seven archetypes by using within-subject models.
dc.format.extent19 p.
dc.language.isoeng
dc.publisherTaylor & Francis
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Salut mental::Psicologia
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject.lcshHuman-machine systems
dc.subject.lcshHuman-computer interaction
dc.subject.lcshCognitive psyhchology
dc.subject.otherAffective computing
dc.subject.otherPsychology
dc.subject.otherUnconscious
dc.subject.otherModelling
dc.subject.otherArchetypes
dc.subject.otherHuman-computer interaction
dc.subject.otherHeart-rate
dc.subject.otherTime-series
dc.subject.otherArchetypes
dc.subject.otherEmotion
dc.subject.otherLDA
dc.subject.otherRecognition
dc.subject.otherExperience
dc.subject.otherResponses
dc.subject.otherMemory
dc.titleBeyond cognition and affect: sensing the unconscious
dc.typeArticle
dc.subject.lemacSistemes persona-màquina
dc.subject.lemacInteracció persona-ordinador
dc.subject.lemacPsicologia de la cognició
dc.contributor.groupUniversitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.contributor.groupUniversitat Politècnica de Catalunya. ISSET - Integrated Smart Sensors and Health Technologies
dc.identifier.doi10.1080/0144929X.2014.912353
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.tandfonline.com/doi/abs/10.1080/0144929X.2014.912353#.VCL7K_l_t9g
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac15184455
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorIvonin, L.; Huang-Ming, C.; Diaz, M.; Catala, A.; Chen, W.; Rauterberg, M.
local.citation.publicationNameBehaviour & information technology
local.citation.volume34
local.citation.number3
local.citation.startingPage220
local.citation.endingPage238


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